from numpy import *

'''
SMO 辅助函数
loadDataSet（读取文件）
selectJrand（从0~m中随机选一个不为i的数）
clipAlpha（调整alpha的值）
'''
def loadDataSet(fileName):
    dataMat = []; labelMat = []
    fr = open(fileName)
    for line in fr.readlines():
        lineArr = line.strip().split('\t')
        dataMat.append([float(lineArr[0]), float(lineArr[1])])
        labelMat.append(float(lineArr[2]))
    return dataMat, labelMat

def selectJrand(i, m):
    j = i
    while j == i:
        j = int(random.uniform(0, m))
    return j

def clipAlpha(aj, H, L):
    if aj > H:
        aj = H
    if L < aj:
        aj = L
    return aj

'''
功能：SMO算法简化版
输入：dataMatIn（样本属性） classLabels（样本类别） C（常数） toler（容错率） maxIter（退出前最大迭代次数）
输出：最优alpha和b
'''
def smoSimple(dataMatIn, classLabels, C, toler, maxIter):
    dataMatrix = mat(dataMatIn); labelMat = mat(classLabels).T
    b = 0; m, n = shape(dataMatrix)
    alphas = mat(zeros((m, 1)))
    iter = 0
    while iter < maxIter:
        alphaPairsChanged = 0
        for i in range(m):
            fxi = multiply(alphas, labelMat).T * (dataMatrix * dataMatrix[i:].T) + b             # i的预测类别
            Ei = fxi - float(labelMat[i])
            if ((labelMat[i] * Ei < -toler) and (alphas[i] < C)) or ((labelMat[i] * Ei > toler) and (alphas > 0)):
                j = selectJrand(i, m)
                fxj = float(multiply(alphas, labelMat).T * (dataMatrix * dataMatrix[j:].T)) + b
                Ej = fxj - float(labelMat[j])
                alphaIold = alphas[i].copy()
                alphaJold = alphas[j].copy()
                if labelMat[i] != labelMat[j]:
                    L = max(0, alphas[j] - alphas[i])                   # L 和 H用于调整alpha[j]调整到0到C之间
                    H = min(C, C + alphas[j] - alphas[i])
                else:
                    L = max(0, alphas[j] + alphas[i] - C)
                    H = min(C, alphas[j] + alphas[i])
                if L == H:
                    print("L == H")
                    continue
                eta = 2.0 * dataMatrix[i, :] * dataMatrix[j, :].T - dataMatrix[i, :] * dataMatrix[i, :].T - \
                    dataMatrix[j, :] * dataMatrix[j, :].T       # eta是alpha[j]的最优修改量
                if eta >= 0:
                    print("eta>=0")
                    continue
                alphas[j] -= labelMat[j] * (Ei - Ej) / eta
                alphas[j] = clipAlpha(alphas[j], H, L)
                if abs(alphas[j] - alphaJold < 0.00001):
                    print("j not moving enough")
                    continue
                alphas[i] += labelMat[j] * labelMat[i] * (alphaJold - alphaIold)
                b1 = b - Ei - labelMat[i] * (alphas[i] - alphaIold) * dataMatrix[i, :] * dataMatrix[i, :].T - \
                    labelMat[j] * (alphas[j] - alphaJold) * dataMatrix[i, :] * dataMatrix[j, :].T
                b2 = b - Ej - labelMat[i] * (alphas[i] - alphaIold) * dataMatrix[i, :] * dataMatrix[j, :].T - \
                    labelMat[j] * (alphas[j] - alphaJold) * dataMatrix[j, :] * dataMatrix[j, :].T
                if (0 < alphas[i]) and (C > alphas[i]):
                    b = b1
                elif (0 < alphas[j]) and (C > alphas[j]):
                    b = b2
                else:
                    b = (b1 + b2)/2.0
                alphaPairsChanged += 1
                print("iter: %d i:%d, pairs changed %d" % iter, i, alphaPairsChanged)
        if (alphaPairsChanged == 0):
            iter += 1
        else:
            iter = 0
        print("iteration number: %d" % iter)
    return b, alphas


if __name__ == '__main__':
    dataMat, labelMat = loadDataSet('testSet.txt')
    b, alphas = smoSimple(dataMat, labelMat, 0.6, 0.001, 40)
    print(b, alphas)

